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US 2025/0156640 A1Published application
Knowledge graph entities from text
AssigneeSap SE
InventorHans-Martin Ramsl
Life of this patent
Estimating the term…
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AssigneeSap SE
InventorHans-Martin Ramsl
Estimating the term…
Example methods and systems are directed to generating knowledge graph entities from text. Natural language text is received as input and processed using named entity recognition (NER), part of speech (POS) recognition, and business object recognition (BOR). The outputs of the NER, POS, and BOR processes are combined to generate knowledge entity triples comprising two entities and a relationship between them. Keywords are extracted from the text using NER to generate a set of entities. A node in a knowledge graph is created for at least some of the entities. A POS tagger identifies verbs in the text, generating a set of verbs. Relational verbs (e.g., “talk to” or “communicated with”) are detected and used to create edges in the knowledge graph. The knowledge graph may be converted back to natural language text using a trained machine learning model.
A system comprising:
A method comprising:
A non-transitory computer-readable medium that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
Dependencies are inferred from the claim text.